#!/bin/bash
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# Copyright 2016-17 Vimal Manohar
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# 2017 Nagendra Kumar Goel
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# Apache 2.0.
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# This script does nnet3-based speech activity detection given an input
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# kaldi data directory and outputs a segmented kaldi data directory.
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# This script can also do music detection and other similar segmentation
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# using appropriate options such as --output-name output-music.
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set -e
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set -o pipefail
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set -u
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if [ -f ./path.sh ]; then . ./path.sh; fi
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#export PATH=/usr/local/cuda-10.0/bin:$PATH
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#export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64:$LD_LIBRARY_PATH
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#echo $PATH
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#echo $LD_LIBRARY_PATH
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affix= # Affix for the segmentation
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nj=32
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cmd=run.pl
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stage=-1
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# Feature options (Must match training)
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mfcc_config=conf/mfcc_hires.conf
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feat_affix= # Affix for the type of feature used
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output_name=output # The output node in the network
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sad_name=sad # Base name for the directory storing the computed loglikes
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# Can be music for music detection
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segmentation_name=segmentation # Base name for the directory doing segmentation
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# Can be segmentation_music for music detection
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# SAD network config
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iter=final # Model iteration to use
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# Contexts must ideally match training for LSTM models, but
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# may not necessarily for stats components
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extra_left_context=0 # Set to some large value, typically 40 for LSTM (must match training)
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extra_right_context=0
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extra_left_context_initial=-1
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extra_right_context_final=-1
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frames_per_chunk=150
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# Decoding options
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graph_opts="--min-silence-duration=0.03 --min-speech-duration=0.3 --max-speech-duration=10.0"
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acwt=0.3
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# These <from>_in_<to>_weight represent the fraction of <from> probability
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# to transfer to <to> class.
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# e.g. --speech-in-sil-weight=0.0 --garbage-in-sil-weight=0.0 --sil-in-speech-weight=0.0 --garbage-in-speech-weight=0.3
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transform_probs_opts=""
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# Postprocessing options
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segment_padding=0.2 # Duration (in seconds) of padding added to segments
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min_segment_dur=0 # Minimum duration (in seconds) required for a segment to be included
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# This is before any padding. Segments shorter than this duration will be removed.
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# This is an alternative to --min-speech-duration above.
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merge_consecutive_max_dur=0 # Merge consecutive segments as long as the merged segment is no longer than this many
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# seconds. The segments are only merged if their boundaries are touching.
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# This is after padding by --segment-padding seconds.
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# 0 means do not merge. Use 'inf' to not limit the duration.
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echo $*
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. utils/parse_options.sh
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if [ $# -ne 5 ]; then
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echo "This script does nnet3-based speech activity detection given an input kaldi "
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echo "data directory and outputs an output kaldi data directory."
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echo "See script for details of the options to be supplied."
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echo "Usage: $0 <src-data-dir> <sad-nnet-dir> <mfcc-dir> <work-dir> <out-data-dir>"
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echo " e.g.: $0 ~/workspace/egs/ami/s5b/data/sdm1/dev exp/nnet3_sad_snr/nnet_tdnn_j_n4 \\"
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echo " mfcc_hires exp/segmentation_sad_snr/nnet_tdnn_j_n4 data/ami_sdm1_dev"
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echo ""
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echo "Options: "
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echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
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echo " --nj <num-job> # number of parallel jobs to run."
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echo " --stage <stage> # stage to do partial re-run from."
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echo " --convert-data-dir-to-whole <true|false> # If true, the input data directory is "
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echo " # first converted to whole data directory (i.e. whole recordings) "
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echo " # and segmentation is done on that."
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echo " # If false, then the original segments are "
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echo " # retained and they are split into sub-segments."
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echo " --output-name <name> # The output node in the network"
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echo " --extra-left-context <context|0> # Set to some large value, typically 40 for LSTM (must match training)"
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echo " --extra-right-context <context|0> # For BLSTM or statistics pooling"
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exit 1
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fi
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src_data_dir=$1 # The input data directory that needs to be segmented.
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# If convert_data_dir_to_whole is true, any segments in that will be ignored.
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sad_nnet_dir=$2 # The SAD neural network
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mfcc_dir=$3 # The directory to store the features
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dir=$4 # Work directory
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data_dir=$5 # The output data directory will be ${data_dir}_seg
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affix=${affix:+_$affix}
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feat_affix=${feat_affix:+_$feat_affix}
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data_id=`basename $data_dir`
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sad_dir=${dir}/${sad_name}${affix}_${data_id}${feat_affix}
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seg_dir=${dir}/${segmentation_name}${affix}_${data_id}${feat_affix}
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# test_data_dir=data/${data_id}${feat_affix}
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test_data_dir=${src_data_dir}
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###############################################################################
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## Forward pass through the network network and dump the log-likelihoods.
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###############################################################################
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frame_subsampling_factor=1
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if [ -f $sad_nnet_dir/frame_subsampling_factor ]; then
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frame_subsampling_factor=$(cat $sad_nnet_dir/frame_subsampling_factor)
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fi
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mkdir -p $dir
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if [ $stage -le 1 ]; then
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if [ "$(readlink -f $sad_nnet_dir)" != "$(readlink -f $dir)" ]; then
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cp $sad_nnet_dir/cmvn_opts $dir || exit 1
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fi
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########################################################################
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## Initialize neural network for decoding using the output $output_name
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########################################################################
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if [ ! -z "$output_name" ] && [ "$output_name" != output ]; then
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$cmd $dir/log/get_nnet_${output_name}.log \
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nnet3-copy --edits="rename-node old-name=$output_name new-name=output" \
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$sad_nnet_dir/$iter.raw $dir/${iter}_${output_name}.raw || exit 1
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iter=${iter}_${output_name}
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else
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if ! diff $sad_nnet_dir/$iter.raw $dir/$iter.raw; then
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cp $sad_nnet_dir/$iter.raw $dir/
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fi
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fi
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echo ${test_data_dir}
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steps/nnet3/compute_output.sh --nj $nj --cmd "$cmd" \
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--iter ${iter} \
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--extra-left-context $extra_left_context \
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--extra-right-context $extra_right_context \
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--extra-left-context-initial $extra_left_context_initial \
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--extra-right-context-final $extra_right_context_final \
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--frames-per-chunk $frames_per_chunk --apply-exp true \
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--frame-subsampling-factor $frame_subsampling_factor \
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${test_data_dir} $dir $sad_dir || exit 1
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fi
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###############################################################################
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## Prepare FST we search to make speech/silence decisions.
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###############################################################################
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utils/data/get_utt2dur.sh --nj $nj --cmd "$cmd" $test_data_dir || exit 1
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frame_shift=$(utils/data/get_frame_shift.sh $test_data_dir) || exit 1
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graph_dir=${dir}/graph_${output_name}
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if [ $stage -le 2 ]; then
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mkdir -p $graph_dir
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# 1 for silence and 2 for speech
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cat <<EOF > $graph_dir/words.txt
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<eps> 0
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silence 1
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speech 2
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EOF
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$cmd $graph_dir/log/make_graph.log \
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steps/segmentation/internal/prepare_sad_graph.py $graph_opts \
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--frame-shift=$(perl -e "print $frame_shift * $frame_subsampling_factor") - \| \
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fstcompile --isymbols=$graph_dir/words.txt --osymbols=$graph_dir/words.txt '>' \
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$graph_dir/HCLG.fst
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fi
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###############################################################################
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## Do Viterbi decoding to create per-frame alignments.
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###############################################################################
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post_vec=$sad_nnet_dir/post_${output_name}.vec
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if [ ! -f $sad_nnet_dir/post_${output_name}.vec ]; then
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if [ ! -f $sad_nnet_dir/post_${output_name}.txt ]; then
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echo "$0: Could not find $sad_nnet_dir/post_${output_name}.vec. "
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echo "Re-run the corresponding stage in the training script possibly "
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echo "with --compute-average-posteriors=true or compute the priors "
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echo "from the training labels"
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exit 1
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else
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post_vec=$sad_nnet_dir/post_${output_name}.txt
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fi
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fi
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mkdir -p $seg_dir
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if [ $stage -le 3 ]; then
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steps/segmentation/internal/get_transform_probs_mat.py \
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--priors="$post_vec" $transform_probs_opts > $seg_dir/transform_probs.mat
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steps/segmentation/decode_sad.sh --acwt $acwt --cmd "$cmd" \
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--nj $nj \
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--transform "$seg_dir/transform_probs.mat" \
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$graph_dir $sad_dir $seg_dir
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fi
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###############################################################################
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## Post-process segmentation to create kaldi data directory.
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###############################################################################
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if [ $stage -le 4 ]; then
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steps/segmentation/post_process_sad_to_segments.sh \
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--segment-padding $segment_padding --min-segment-dur $min_segment_dur \
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--merge-consecutive-max-dur $merge_consecutive_max_dur \
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--cmd "$cmd" --frame-shift $(perl -e "print $frame_subsampling_factor * $frame_shift") \
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${test_data_dir} ${seg_dir} ${seg_dir}
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fi
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if [ $stage -le 5 ]; then
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utils/data/subsegment_data_dir.sh ${test_data_dir} ${seg_dir}/segments \
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${data_dir}_seg
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fi
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echo "$0: Created output segmented kaldi data directory in ${data_dir}_seg"
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exit 0
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